Overview

Brought to you by YData

Dataset statistics

Number of variables17
Number of observations260920
Missing cells994301
Missing cells (%)22.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory125.9 MiB
Average record size in memory506.1 B

Variable types

Numeric7
Categorical5
Text5

Alerts

batsman_runs is highly overall correlated with total_runsHigh correlation
dismissal_kind is highly overall correlated with extras_type and 1 other fieldsHigh correlation
extras_type is highly overall correlated with dismissal_kindHigh correlation
is_wicket is highly overall correlated with dismissal_kindHigh correlation
total_runs is highly overall correlated with batsman_runsHigh correlation
is_wicket is highly imbalanced (71.5%) Imbalance
extras_type has 246795 (94.6%) missing values Missing
player_dismissed has 247970 (95.0%) missing values Missing
dismissal_kind has 247970 (95.0%) missing values Missing
fielder has 251566 (96.4%) missing values Missing
over has 13906 (5.3%) zeros Zeros
batsman_runs has 103940 (39.8%) zeros Zeros
extra_runs has 246795 (94.6%) zeros Zeros
total_runs has 90438 (34.7%) zeros Zeros

Reproduction

Analysis started2025-04-11 11:40:17.549358
Analysis finished2025-04-11 11:40:26.374368
Duration8.83 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

match_id
Real number (ℝ)

Distinct1095
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean907066.51
Minimum335982
Maximum1426312
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2025-04-11T13:40:26.432918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum335982
5-th percentile336039
Q1548334
median980967
Q31254066
95-th percentile1422135
Maximum1426312
Range1090330
Interquartile range (IQR)705732

Descriptive statistics

Standard deviation367991.28
Coefficient of variation (CV)0.40569382
Kurtosis-1.5257974
Mean907066.51
Median Absolute Deviation (MAD)331230
Skewness-0.14529399
Sum2.3667179 × 1011
Variance1.3541758 × 1011
MonotonicityIncreasing
2025-04-11T13:40:26.509983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1216517 269
 
0.1%
392190 267
 
0.1%
1426268 265
 
0.1%
1082625 263
 
0.1%
729315 262
 
0.1%
829737 262
 
0.1%
598004 261
 
0.1%
1254077 260
 
0.1%
1359480 260
 
0.1%
1426288 260
 
0.1%
Other values (1085) 258291
99.0%
ValueCountFrequency (%)
335982 225
0.1%
335983 248
0.1%
335984 219
0.1%
335985 246
0.1%
335986 240
0.1%
335987 241
0.1%
335988 205
0.1%
335989 255
0.1%
335990 248
0.1%
335991 250
0.1%
ValueCountFrequency (%)
1426312 184
0.1%
1426311 251
0.1%
1426310 241
0.1%
1426309 208
0.1%
1426307 247
0.1%
1426306 253
0.1%
1426305 259
0.1%
1426303 235
0.1%
1426302 253
0.1%
1426300 247
0.1%

inning
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4835314
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2025-04-11T13:40:26.560527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile2
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5026432
Coefficient of variation (CV)0.33881535
Kurtosis-1.6485018
Mean1.4835314
Median Absolute Deviation (MAD)0
Skewness0.12647854
Sum387083
Variance0.25265019
MonotonicityNot monotonic
2025-04-11T13:40:26.605064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 135018
51.7%
2 125741
48.2%
3 77
 
< 0.1%
4 72
 
< 0.1%
5 8
 
< 0.1%
6 4
 
< 0.1%
ValueCountFrequency (%)
1 135018
51.7%
2 125741
48.2%
3 77
 
< 0.1%
4 72
 
< 0.1%
5 8
 
< 0.1%
6 4
 
< 0.1%
ValueCountFrequency (%)
6 4
 
< 0.1%
5 8
 
< 0.1%
4 72
 
< 0.1%
3 77
 
< 0.1%
2 125741
48.2%
1 135018
51.7%

batting_team
Categorical

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.6 MiB
Mumbai Indians
31437 
Kolkata Knight Riders
29514 
Chennai Super Kings
28651 
Royal Challengers Bangalore
28205 
Rajasthan Royals
26242 
Other values (14)
116871 

Length

Max length27
Median length22
Mean length17.908248
Min length12

Characters and Unicode

Total characters4672620
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKolkata Knight Riders
2nd rowKolkata Knight Riders
3rd rowKolkata Knight Riders
4th rowKolkata Knight Riders
5th rowKolkata Knight Riders

Common Values

ValueCountFrequency (%)
Mumbai Indians 31437
12.0%
Kolkata Knight Riders 29514
11.3%
Chennai Super Kings 28651
11.0%
Royal Challengers Bangalore 28205
10.8%
Rajasthan Royals 26242
10.1%
Kings XI Punjab 22646
8.7%
Sunrisers Hyderabad 21843
8.4%
Delhi Daredevils 18786
7.2%
Delhi Capitals 10946
 
4.2%
Deccan Chargers 9034
 
3.5%
Other values (9) 33616
12.9%

Length

2025-04-11T13:40:26.663615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
kings 58130
 
9.0%
super 34051
 
5.3%
mumbai 31437
 
4.9%
indians 31437
 
4.9%
challengers 30023
 
4.7%
royal 30023
 
4.7%
delhi 29732
 
4.6%
kolkata 29514
 
4.6%
knight 29514
 
4.6%
riders 29514
 
4.6%
Other values (26) 309761
48.2%

Most occurring characters

ValueCountFrequency (%)
a 546807
 
11.7%
n 390207
 
8.4%
382216
 
8.2%
e 326910
 
7.0%
i 321915
 
6.9%
s 312167
 
6.7%
r 258027
 
5.5%
l 236894
 
5.1%
g 163684
 
3.5%
h 154778
 
3.3%
Other values (28) 1579015
33.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4672620
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 546807
 
11.7%
n 390207
 
8.4%
382216
 
8.2%
e 326910
 
7.0%
i 321915
 
6.9%
s 312167
 
6.7%
r 258027
 
5.5%
l 236894
 
5.1%
g 163684
 
3.5%
h 154778
 
3.3%
Other values (28) 1579015
33.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4672620
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 546807
 
11.7%
n 390207
 
8.4%
382216
 
8.2%
e 326910
 
7.0%
i 321915
 
6.9%
s 312167
 
6.7%
r 258027
 
5.5%
l 236894
 
5.1%
g 163684
 
3.5%
h 154778
 
3.3%
Other values (28) 1579015
33.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4672620
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 546807
 
11.7%
n 390207
 
8.4%
382216
 
8.2%
e 326910
 
7.0%
i 321915
 
6.9%
s 312167
 
6.7%
r 258027
 
5.5%
l 236894
 
5.1%
g 163684
 
3.5%
h 154778
 
3.3%
Other values (28) 1579015
33.8%

bowling_team
Categorical

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.7 MiB
Mumbai Indians
31505 
Kolkata Knight Riders
29663 
Chennai Super Kings
28576 
Royal Challengers Bangalore
28358 
Rajasthan Royals
26432 
Other values (14)
116386 

Length

Max length27
Median length22
Mean length17.915254
Min length12

Characters and Unicode

Total characters4674448
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRoyal Challengers Bangalore
2nd rowRoyal Challengers Bangalore
3rd rowRoyal Challengers Bangalore
4th rowRoyal Challengers Bangalore
5th rowRoyal Challengers Bangalore

Common Values

ValueCountFrequency (%)
Mumbai Indians 31505
12.1%
Kolkata Knight Riders 29663
11.4%
Chennai Super Kings 28576
11.0%
Royal Challengers Bangalore 28358
10.9%
Rajasthan Royals 26432
10.1%
Kings XI Punjab 22483
8.6%
Sunrisers Hyderabad 21717
8.3%
Delhi Daredevils 18725
7.2%
Delhi Capitals 11216
 
4.3%
Deccan Chargers 9039
 
3.5%
Other values (9) 33206
12.7%

Length

2025-04-11T13:40:26.722665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
kings 57778
 
9.0%
super 33802
 
5.3%
mumbai 31505
 
4.9%
indians 31505
 
4.9%
challengers 30159
 
4.7%
royal 30159
 
4.7%
delhi 29941
 
4.7%
kolkata 29663
 
4.6%
knight 29663
 
4.6%
riders 29663
 
4.6%
Other values (26) 309266
48.1%

Most occurring characters

ValueCountFrequency (%)
a 547793
 
11.7%
n 389695
 
8.3%
382184
 
8.2%
e 327192
 
7.0%
i 322061
 
6.9%
s 312298
 
6.7%
r 257725
 
5.5%
l 238227
 
5.1%
g 163884
 
3.5%
h 155424
 
3.3%
Other values (28) 1577965
33.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4674448
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 547793
 
11.7%
n 389695
 
8.3%
382184
 
8.2%
e 327192
 
7.0%
i 322061
 
6.9%
s 312298
 
6.7%
r 257725
 
5.5%
l 238227
 
5.1%
g 163884
 
3.5%
h 155424
 
3.3%
Other values (28) 1577965
33.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4674448
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 547793
 
11.7%
n 389695
 
8.3%
382184
 
8.2%
e 327192
 
7.0%
i 322061
 
6.9%
s 312298
 
6.7%
r 257725
 
5.5%
l 238227
 
5.1%
g 163884
 
3.5%
h 155424
 
3.3%
Other values (28) 1577965
33.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4674448
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 547793
 
11.7%
n 389695
 
8.3%
382184
 
8.2%
e 327192
 
7.0%
i 322061
 
6.9%
s 312298
 
6.7%
r 257725
 
5.5%
l 238227
 
5.1%
g 163884
 
3.5%
h 155424
 
3.3%
Other values (28) 1577965
33.8%

over
Real number (ℝ)

Zeros 

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.1976774
Minimum0
Maximum19
Zeros13906
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2025-04-11T13:40:26.771706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median9
Q314
95-th percentile18
Maximum19
Range19
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.6834837
Coefficient of variation (CV)0.61792597
Kurtosis-1.1856269
Mean9.1976774
Median Absolute Deviation (MAD)5
Skewness0.041707947
Sum2399858
Variance32.301987
MonotonicityNot monotonic
2025-04-11T13:40:26.825252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 13906
 
5.3%
1 13773
 
5.3%
2 13597
 
5.2%
3 13575
 
5.2%
4 13560
 
5.2%
5 13494
 
5.2%
6 13452
 
5.2%
7 13430
 
5.1%
8 13396
 
5.1%
9 13354
 
5.1%
Other values (10) 125383
48.1%
ValueCountFrequency (%)
0 13906
5.3%
1 13773
5.3%
2 13597
5.2%
3 13575
5.2%
4 13560
5.2%
5 13494
5.2%
6 13452
5.2%
7 13430
5.1%
8 13396
5.1%
9 13354
5.1%
ValueCountFrequency (%)
19 9998
3.8%
18 11583
4.4%
17 12318
4.7%
16 12685
4.9%
15 12879
4.9%
14 13024
5.0%
13 13124
5.0%
12 13222
5.1%
11 13261
5.1%
10 13289
5.1%

ball
Real number (ℝ)

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6244864
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2025-04-11T13:40:26.876796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q35
95-th percentile6
Maximum11
Range10
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.8149205
Coefficient of variation (CV)0.50073865
Kurtosis-1.053745
Mean3.6244864
Median Absolute Deviation (MAD)2
Skewness0.10712572
Sum945701
Variance3.2939363
MonotonicityNot monotonic
2025-04-11T13:40:26.919333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 42210
16.2%
2 42106
16.1%
3 42002
16.1%
4 41891
16.1%
5 41752
16.0%
6 41619
16.0%
7 7776
 
3.0%
8 1307
 
0.5%
9 225
 
0.1%
10 30
 
< 0.1%
ValueCountFrequency (%)
1 42210
16.2%
2 42106
16.1%
3 42002
16.1%
4 41891
16.1%
5 41752
16.0%
6 41619
16.0%
7 7776
 
3.0%
8 1307
 
0.5%
9 225
 
0.1%
10 30
 
< 0.1%
ValueCountFrequency (%)
11 2
 
< 0.1%
10 30
 
< 0.1%
9 225
 
0.1%
8 1307
 
0.5%
7 7776
 
3.0%
6 41619
16.0%
5 41752
16.0%
4 41891
16.1%
3 42002
16.1%
2 42106
16.1%

batter
Text

Distinct673
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size14.5 MiB
2025-04-11T13:40:27.307163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length23
Median length19
Mean length9.4247854
Min length5

Characters and Unicode

Total characters2459115
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)< 0.1%

Sample

1st rowSC Ganguly
2nd rowBB McCullum
3rd rowBB McCullum
4th rowBB McCullum
5th rowBB McCullum
ValueCountFrequency (%)
s 8992
 
1.7%
v 8965
 
1.7%
sharma 7691
 
1.4%
da 7020
 
1.3%
singh 7014
 
1.3%
kohli 6256
 
1.2%
de 6231
 
1.2%
r 6145
 
1.1%
dhawan 5671
 
1.1%
rg 5185
 
1.0%
Other values (894) 467539
87.1%
2025-04-11T13:40:27.610921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 278809
 
11.3%
275789
 
11.2%
i 119472
 
4.9%
h 113155
 
4.6%
n 112566
 
4.6%
r 107131
 
4.4%
S 100549
 
4.1%
e 99075
 
4.0%
l 95230
 
3.9%
s 69740
 
2.8%
Other values (47) 1087599
44.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2459115
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 278809
 
11.3%
275789
 
11.2%
i 119472
 
4.9%
h 113155
 
4.6%
n 112566
 
4.6%
r 107131
 
4.4%
S 100549
 
4.1%
e 99075
 
4.0%
l 95230
 
3.9%
s 69740
 
2.8%
Other values (47) 1087599
44.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2459115
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 278809
 
11.3%
275789
 
11.2%
i 119472
 
4.9%
h 113155
 
4.6%
n 112566
 
4.6%
r 107131
 
4.4%
S 100549
 
4.1%
e 99075
 
4.0%
l 95230
 
3.9%
s 69740
 
2.8%
Other values (47) 1087599
44.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2459115
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 278809
 
11.3%
275789
 
11.2%
i 119472
 
4.9%
h 113155
 
4.6%
n 112566
 
4.6%
r 107131
 
4.4%
S 100549
 
4.1%
e 99075
 
4.0%
l 95230
 
3.9%
s 69740
 
2.8%
Other values (47) 1087599
44.2%

bowler
Text

Distinct530
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size14.6 MiB
2025-04-11T13:40:27.791074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length23
Median length18
Mean length9.7543423
Min length5

Characters and Unicode

Total characters2545103
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowP Kumar
2nd rowP Kumar
3rd rowP Kumar
4th rowP Kumar
5th rowP Kumar
ValueCountFrequency (%)
r 12556
 
2.4%
sharma 12256
 
2.3%
singh 11252
 
2.1%
a 10113
 
1.9%
kumar 9490
 
1.8%
m 9427
 
1.8%
khan 7437
 
1.4%
s 7205
 
1.4%
patel 6838
 
1.3%
pp 5840
 
1.1%
Other values (729) 438059
82.6%
2025-04-11T13:40:28.038285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 327701
 
12.9%
269553
 
10.6%
h 143316
 
5.6%
r 135369
 
5.3%
n 128319
 
5.0%
e 118583
 
4.7%
i 110331
 
4.3%
S 92216
 
3.6%
l 76231
 
3.0%
M 65127
 
2.6%
Other values (45) 1078357
42.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2545103
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 327701
 
12.9%
269553
 
10.6%
h 143316
 
5.6%
r 135369
 
5.3%
n 128319
 
5.0%
e 118583
 
4.7%
i 110331
 
4.3%
S 92216
 
3.6%
l 76231
 
3.0%
M 65127
 
2.6%
Other values (45) 1078357
42.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2545103
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 327701
 
12.9%
269553
 
10.6%
h 143316
 
5.6%
r 135369
 
5.3%
n 128319
 
5.0%
e 118583
 
4.7%
i 110331
 
4.3%
S 92216
 
3.6%
l 76231
 
3.0%
M 65127
 
2.6%
Other values (45) 1078357
42.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2545103
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 327701
 
12.9%
269553
 
10.6%
h 143316
 
5.6%
r 135369
 
5.3%
n 128319
 
5.0%
e 118583
 
4.7%
i 110331
 
4.3%
S 92216
 
3.6%
l 76231
 
3.0%
M 65127
 
2.6%
Other values (45) 1078357
42.4%
Distinct663
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size14.5 MiB
2025-04-11T13:40:28.204426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length23
Median length19
Mean length9.4367737
Min length5

Characters and Unicode

Total characters2462243
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)< 0.1%

Sample

1st rowBB McCullum
2nd rowSC Ganguly
3rd rowSC Ganguly
4th rowSC Ganguly
5th rowSC Ganguly
ValueCountFrequency (%)
s 9231
 
1.7%
v 8905
 
1.7%
sharma 7804
 
1.5%
da 6787
 
1.3%
singh 6717
 
1.3%
de 6137
 
1.1%
dhawan 6108
 
1.1%
kohli 6089
 
1.1%
r 6058
 
1.1%
mk 5308
 
1.0%
Other values (887) 467943
87.1%
2025-04-11T13:40:28.427615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 280963
 
11.4%
276167
 
11.2%
i 119335
 
4.8%
h 114211
 
4.6%
n 112947
 
4.6%
r 106873
 
4.3%
S 100776
 
4.1%
e 99512
 
4.0%
l 94571
 
3.8%
s 70157
 
2.8%
Other values (47) 1086731
44.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2462243
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 280963
 
11.4%
276167
 
11.2%
i 119335
 
4.8%
h 114211
 
4.6%
n 112947
 
4.6%
r 106873
 
4.3%
S 100776
 
4.1%
e 99512
 
4.0%
l 94571
 
3.8%
s 70157
 
2.8%
Other values (47) 1086731
44.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2462243
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 280963
 
11.4%
276167
 
11.2%
i 119335
 
4.8%
h 114211
 
4.6%
n 112947
 
4.6%
r 106873
 
4.3%
S 100776
 
4.1%
e 99512
 
4.0%
l 94571
 
3.8%
s 70157
 
2.8%
Other values (47) 1086731
44.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2462243
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 280963
 
11.4%
276167
 
11.2%
i 119335
 
4.8%
h 114211
 
4.6%
n 112947
 
4.6%
r 106873
 
4.3%
S 100776
 
4.1%
e 99512
 
4.0%
l 94571
 
3.8%
s 70157
 
2.8%
Other values (47) 1086731
44.1%

batsman_runs
Real number (ℝ)

High correlation  Zeros 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2650008
Minimum0
Maximum6
Zeros103940
Zeros (%)39.8%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2025-04-11T13:40:28.469651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.6392976
Coefficient of variation (CV)1.2958867
Kurtosis1.5226807
Mean1.2650008
Median Absolute Deviation (MAD)1
Skewness1.5642578
Sum330064
Variance2.6872968
MonotonicityNot monotonic
2025-04-11T13:40:28.511186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 103940
39.8%
1 96778
37.1%
4 29850
 
11.4%
2 16453
 
6.3%
6 13051
 
5.0%
3 783
 
0.3%
5 65
 
< 0.1%
ValueCountFrequency (%)
0 103940
39.8%
1 96778
37.1%
2 16453
 
6.3%
3 783
 
0.3%
4 29850
 
11.4%
5 65
 
< 0.1%
6 13051
 
5.0%
ValueCountFrequency (%)
6 13051
 
5.0%
5 65
 
< 0.1%
4 29850
 
11.4%
3 783
 
0.3%
2 16453
 
6.3%
1 96778
37.1%
0 103940
39.8%

extra_runs
Real number (ℝ)

Zeros 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.067806224
Minimum0
Maximum7
Zeros246795
Zeros (%)94.6%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2025-04-11T13:40:28.552221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.34326535
Coefficient of variation (CV)5.062446
Kurtosis90.549735
Mean0.067806224
Median Absolute Deviation (MAD)0
Skewness8.1879494
Sum17692
Variance0.1178311
MonotonicityNot monotonic
2025-04-11T13:40:28.595258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 246795
94.6%
1 12628
 
4.8%
2 585
 
0.2%
4 504
 
0.2%
5 325
 
0.1%
3 82
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
0 246795
94.6%
1 12628
 
4.8%
2 585
 
0.2%
3 82
 
< 0.1%
4 504
 
0.2%
5 325
 
0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
7 1
 
< 0.1%
5 325
 
0.1%
4 504
 
0.2%
3 82
 
< 0.1%
2 585
 
0.2%
1 12628
 
4.8%
0 246795
94.6%

total_runs
Real number (ℝ)

High correlation  Zeros 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.332807
Minimum0
Maximum7
Zeros90438
Zeros (%)34.7%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2025-04-11T13:40:28.637295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.6264158
Coefficient of variation (CV)1.2202936
Kurtosis1.4714525
Mean1.332807
Median Absolute Deviation (MAD)1
Skewness1.5363123
Sum347756
Variance2.6452285
MonotonicityNot monotonic
2025-04-11T13:40:28.680831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 108440
41.6%
0 90438
34.7%
4 30221
 
11.6%
2 17323
 
6.6%
6 12964
 
5.0%
3 922
 
0.4%
5 524
 
0.2%
7 88
 
< 0.1%
ValueCountFrequency (%)
0 90438
34.7%
1 108440
41.6%
2 17323
 
6.6%
3 922
 
0.4%
4 30221
 
11.6%
5 524
 
0.2%
6 12964
 
5.0%
7 88
 
< 0.1%
ValueCountFrequency (%)
7 88
 
< 0.1%
6 12964
 
5.0%
5 524
 
0.2%
4 30221
 
11.6%
3 922
 
0.4%
2 17323
 
6.6%
1 108440
41.6%
0 90438
34.7%

extras_type
Categorical

High correlation  Missing 

Distinct5
Distinct (%)< 0.1%
Missing246795
Missing (%)94.6%
Memory size13.9 MiB
wides
8380 
legbyes
4001 
noballs
1069 
byes
 
673
penalty
 
2

Length

Max length7
Median length5
Mean length5.6705133
Min length4

Characters and Unicode

Total characters80096
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowlegbyes
2nd rowwides
3rd rowlegbyes
4th rowlegbyes
5th rowwides

Common Values

ValueCountFrequency (%)
wides 8380
 
3.2%
legbyes 4001
 
1.5%
noballs 1069
 
0.4%
byes 673
 
0.3%
penalty 2
 
< 0.1%
(Missing) 246795
94.6%

Length

2025-04-11T13:40:28.735377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-11T13:40:28.779415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
wides 8380
59.3%
legbyes 4001
28.3%
noballs 1069
 
7.6%
byes 673
 
4.8%
penalty 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 17057
21.3%
s 14123
17.6%
i 8380
10.5%
w 8380
10.5%
d 8380
10.5%
l 6141
 
7.7%
b 5743
 
7.2%
y 4676
 
5.8%
g 4001
 
5.0%
n 1071
 
1.3%
Other values (4) 2144
 
2.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 80096
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 17057
21.3%
s 14123
17.6%
i 8380
10.5%
w 8380
10.5%
d 8380
10.5%
l 6141
 
7.7%
b 5743
 
7.2%
y 4676
 
5.8%
g 4001
 
5.0%
n 1071
 
1.3%
Other values (4) 2144
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 80096
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 17057
21.3%
s 14123
17.6%
i 8380
10.5%
w 8380
10.5%
d 8380
10.5%
l 6141
 
7.7%
b 5743
 
7.2%
y 4676
 
5.8%
g 4001
 
5.0%
n 1071
 
1.3%
Other values (4) 2144
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 80096
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 17057
21.3%
s 14123
17.6%
i 8380
10.5%
w 8380
10.5%
d 8380
10.5%
l 6141
 
7.7%
b 5743
 
7.2%
y 4676
 
5.8%
g 4001
 
5.0%
n 1071
 
1.3%
Other values (4) 2144
 
2.7%

is_wicket
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.4 MiB
0
247970 
1
 
12950

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters260920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 247970
95.0%
1 12950
 
5.0%

Length

2025-04-11T13:40:28.832459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-11T13:40:28.864487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 247970
95.0%
1 12950
 
5.0%

Most occurring characters

ValueCountFrequency (%)
0 247970
95.0%
1 12950
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 260920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 247970
95.0%
1 12950
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 260920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 247970
95.0%
1 12950
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 260920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 247970
95.0%
1 12950
 
5.0%

player_dismissed
Text

Missing 

Distinct629
Distinct (%)4.9%
Missing247970
Missing (%)95.0%
Memory size8.3 MiB
2025-04-11T13:40:28.988093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length23
Median length19
Mean length9.4804633
Min length5

Characters and Unicode

Total characters122772
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique103 ?
Unique (%)0.8%

Sample

1st rowSC Ganguly
2nd rowRT Ponting
3rd rowDJ Hussey
4th rowR Dravid
5th rowV Kohli
ValueCountFrequency (%)
singh 434
 
1.6%
sharma 409
 
1.5%
s 388
 
1.5%
r 379
 
1.4%
v 351
 
1.3%
m 277
 
1.0%
de 260
 
1.0%
da 246
 
0.9%
patel 245
 
0.9%
dj 229
 
0.9%
Other values (843) 23377
87.9%
2025-04-11T13:40:29.193768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 14253
 
11.6%
13645
 
11.1%
h 5894
 
4.8%
i 5881
 
4.8%
n 5626
 
4.6%
r 5512
 
4.5%
S 4939
 
4.0%
e 4938
 
4.0%
l 4421
 
3.6%
s 3328
 
2.7%
Other values (47) 54335
44.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 122772
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 14253
 
11.6%
13645
 
11.1%
h 5894
 
4.8%
i 5881
 
4.8%
n 5626
 
4.6%
r 5512
 
4.5%
S 4939
 
4.0%
e 4938
 
4.0%
l 4421
 
3.6%
s 3328
 
2.7%
Other values (47) 54335
44.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 122772
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 14253
 
11.6%
13645
 
11.1%
h 5894
 
4.8%
i 5881
 
4.8%
n 5626
 
4.6%
r 5512
 
4.5%
S 4939
 
4.0%
e 4938
 
4.0%
l 4421
 
3.6%
s 3328
 
2.7%
Other values (47) 54335
44.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 122772
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 14253
 
11.6%
13645
 
11.1%
h 5894
 
4.8%
i 5881
 
4.8%
n 5626
 
4.6%
r 5512
 
4.5%
S 4939
 
4.0%
e 4938
 
4.0%
l 4421
 
3.6%
s 3328
 
2.7%
Other values (47) 54335
44.3%

dismissal_kind
Categorical

High correlation  Missing 

Distinct10
Distinct (%)0.1%
Missing247970
Missing (%)95.0%
Memory size13.9 MiB
caught
8063 
bowled
2212 
run out
1114 
lbw
 
800
caught and bowled
 
367
Other values (5)
 
394

Length

Max length21
Median length6
Mean length6.2562934
Min length3

Characters and Unicode

Total characters81019
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcaught
2nd rowcaught
3rd rowcaught
4th rowbowled
5th rowbowled

Common Values

ValueCountFrequency (%)
caught 8063
 
3.1%
bowled 2212
 
0.8%
run out 1114
 
0.4%
lbw 800
 
0.3%
caught and bowled 367
 
0.1%
stumped 358
 
0.1%
retired hurt 15
 
< 0.1%
hit wicket 15
 
< 0.1%
obstructing the field 3
 
< 0.1%
retired out 3
 
< 0.1%
(Missing) 247970
95.0%

Length

2025-04-11T13:40:29.253318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-11T13:40:29.309866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
caught 8430
56.8%
bowled 2579
 
17.4%
out 1117
 
7.5%
run 1114
 
7.5%
lbw 800
 
5.4%
and 367
 
2.5%
stumped 358
 
2.4%
retired 18
 
0.1%
hurt 15
 
0.1%
hit 15
 
0.1%
Other values (4) 24
 
0.2%

Most occurring characters

ValueCountFrequency (%)
u 11037
13.6%
t 9977
12.3%
a 8797
10.9%
h 8463
10.4%
c 8448
10.4%
g 8433
10.4%
o 3699
 
4.6%
w 3394
 
4.2%
b 3382
 
4.2%
l 3382
 
4.2%
Other values (11) 12007
14.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 81019
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
u 11037
13.6%
t 9977
12.3%
a 8797
10.9%
h 8463
10.4%
c 8448
10.4%
g 8433
10.4%
o 3699
 
4.6%
w 3394
 
4.2%
b 3382
 
4.2%
l 3382
 
4.2%
Other values (11) 12007
14.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 81019
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
u 11037
13.6%
t 9977
12.3%
a 8797
10.9%
h 8463
10.4%
c 8448
10.4%
g 8433
10.4%
o 3699
 
4.6%
w 3394
 
4.2%
b 3382
 
4.2%
l 3382
 
4.2%
Other values (11) 12007
14.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 81019
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
u 11037
13.6%
t 9977
12.3%
a 8797
10.9%
h 8463
10.4%
c 8448
10.4%
g 8433
10.4%
o 3699
 
4.6%
w 3394
 
4.2%
b 3382
 
4.2%
l 3382
 
4.2%
Other values (11) 12007
14.8%

fielder
Text

Missing 

Distinct607
Distinct (%)6.5%
Missing251566
Missing (%)96.4%
Memory size8.2 MiB
2025-04-11T13:40:29.467500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length23
Median length18
Mean length9.5244815
Min length5

Characters and Unicode

Total characters89092
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique85 ?
Unique (%)0.9%

Sample

1st rowJH Kallis
2nd rowP Kumar
3rd rowCL White
4th rowM Kartik
5th rowRT Ponting
ValueCountFrequency (%)
r 308
 
1.6%
singh 290
 
1.5%
sharma 281
 
1.5%
ms 265
 
1.4%
de 247
 
1.3%
m 244
 
1.3%
dhoni 220
 
1.1%
patel 217
 
1.1%
karthik 213
 
1.1%
s 212
 
1.1%
Other values (817) 16738
87.0%
2025-04-11T13:40:29.666669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 10506
 
11.8%
9881
 
11.1%
h 4485
 
5.0%
i 4413
 
5.0%
r 4031
 
4.5%
n 3988
 
4.5%
e 3615
 
4.1%
S 3489
 
3.9%
l 3151
 
3.5%
K 2295
 
2.6%
Other values (43) 39238
44.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 89092
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 10506
 
11.8%
9881
 
11.1%
h 4485
 
5.0%
i 4413
 
5.0%
r 4031
 
4.5%
n 3988
 
4.5%
e 3615
 
4.1%
S 3489
 
3.9%
l 3151
 
3.5%
K 2295
 
2.6%
Other values (43) 39238
44.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 89092
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 10506
 
11.8%
9881
 
11.1%
h 4485
 
5.0%
i 4413
 
5.0%
r 4031
 
4.5%
n 3988
 
4.5%
e 3615
 
4.1%
S 3489
 
3.9%
l 3151
 
3.5%
K 2295
 
2.6%
Other values (43) 39238
44.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 89092
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 10506
 
11.8%
9881
 
11.1%
h 4485
 
5.0%
i 4413
 
5.0%
r 4031
 
4.5%
n 3988
 
4.5%
e 3615
 
4.1%
S 3489
 
3.9%
l 3151
 
3.5%
K 2295
 
2.6%
Other values (43) 39238
44.0%

Interactions

2025-04-11T13:40:24.986187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:40:21.774955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:40:22.282887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:40:22.864882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:40:23.381822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:40:23.865234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:40:24.375175image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:40:25.061251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:40:21.848518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:40:22.352947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:40:22.938445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:40:23.450381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:40:23.938796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:40:24.550817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:40:25.133812image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:40:21.920078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:40:22.421005image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:40:23.009505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:40:23.517939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:40:24.009857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:40:24.621377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:40:25.214382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:40:21.994641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:40:22.575637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:40:23.085570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:40:23.588498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:40:24.085925image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:40:24.696942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:40:25.286443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:40:22.064201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:40:22.643193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:40:23.156131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:40:23.655555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:40:24.156482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:40:24.767501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:40:25.370514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:40:22.136262image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:40:22.719259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:40:23.231694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:40:23.724614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:40:24.229043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:40:24.839062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:40:25.445579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:40:22.210325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:40:22.793321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:40:23.306758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:40:23.795174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:40:24.302605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-11T13:40:24.912124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-04-11T13:40:29.712208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ballbatsman_runsbatting_teambowling_teamdismissal_kindextra_runsextras_typeinningis_wicketmatch_idovertotal_runs
ball1.0000.0070.0040.0000.026-0.0010.024-0.0050.0030.004-0.0020.006
batsman_runs0.0071.0000.0170.0130.363-0.2450.375-0.0100.2670.0320.1200.939
batting_team0.0040.0171.0000.1330.0250.0050.0400.0330.0100.3290.0000.016
bowling_team0.0000.0130.1331.0000.0250.0070.0310.0350.0050.3290.0000.012
dismissal_kind0.0260.3630.0250.0251.0000.2600.6280.0181.0000.0340.0670.362
extra_runs-0.001-0.2450.0050.0070.2601.0000.395-0.0010.050-0.0020.0170.090
extras_type0.0240.3750.0400.0310.6280.3951.0000.0170.0330.0500.0810.292
inning-0.005-0.0100.0330.0350.018-0.0010.0171.0000.0160.001-0.047-0.010
is_wicket0.0030.2670.0100.0051.0000.0500.0330.0161.0000.0030.0910.297
match_id0.0040.0320.3290.3290.034-0.0020.0500.0010.0031.0000.0110.031
over-0.0020.1200.0000.0000.0670.0170.081-0.0470.0910.0111.0000.125
total_runs0.0060.9390.0160.0120.3620.0900.292-0.0100.2970.0310.1251.000

Missing values

2025-04-11T13:40:25.577190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-11T13:40:25.818896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-04-11T13:40:26.212230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

match_idinningbatting_teambowling_teamoverballbatterbowlernon_strikerbatsman_runsextra_runstotal_runsextras_typeis_wicketplayer_dismisseddismissal_kindfielder
03359821Kolkata Knight RidersRoyal Challengers Bangalore01SC GangulyP KumarBB McCullum011legbyes0NaNNaNNaN
13359821Kolkata Knight RidersRoyal Challengers Bangalore02BB McCullumP KumarSC Ganguly000NaN0NaNNaNNaN
23359821Kolkata Knight RidersRoyal Challengers Bangalore03BB McCullumP KumarSC Ganguly011wides0NaNNaNNaN
33359821Kolkata Knight RidersRoyal Challengers Bangalore04BB McCullumP KumarSC Ganguly000NaN0NaNNaNNaN
43359821Kolkata Knight RidersRoyal Challengers Bangalore05BB McCullumP KumarSC Ganguly000NaN0NaNNaNNaN
53359821Kolkata Knight RidersRoyal Challengers Bangalore06BB McCullumP KumarSC Ganguly000NaN0NaNNaNNaN
63359821Kolkata Knight RidersRoyal Challengers Bangalore07BB McCullumP KumarSC Ganguly011legbyes0NaNNaNNaN
73359821Kolkata Knight RidersRoyal Challengers Bangalore11BB McCullumZ KhanSC Ganguly000NaN0NaNNaNNaN
83359821Kolkata Knight RidersRoyal Challengers Bangalore12BB McCullumZ KhanSC Ganguly404NaN0NaNNaNNaN
93359821Kolkata Knight RidersRoyal Challengers Bangalore13BB McCullumZ KhanSC Ganguly404NaN0NaNNaNNaN
match_idinningbatting_teambowling_teamoverballbatterbowlernon_strikerbatsman_runsextra_runstotal_runsextras_typeis_wicketplayer_dismisseddismissal_kindfielder
26091014263122Kolkata Knight RidersSunrisers Hyderabad86SS IyerShahbaz AhmedVR Iyer404NaN0NaNNaNNaN
26091114263122Kolkata Knight RidersSunrisers Hyderabad91VR IyerAK MarkramSS Iyer202NaN0NaNNaNNaN
26091214263122Kolkata Knight RidersSunrisers Hyderabad92VR IyerAK MarkramSS Iyer000NaN0NaNNaNNaN
26091314263122Kolkata Knight RidersSunrisers Hyderabad93VR IyerAK MarkramSS Iyer000NaN0NaNNaNNaN
26091414263122Kolkata Knight RidersSunrisers Hyderabad94VR IyerAK MarkramSS Iyer101NaN0NaNNaNNaN
26091514263122Kolkata Knight RidersSunrisers Hyderabad95SS IyerAK MarkramVR Iyer101NaN0NaNNaNNaN
26091614263122Kolkata Knight RidersSunrisers Hyderabad96VR IyerAK MarkramSS Iyer101NaN0NaNNaNNaN
26091714263122Kolkata Knight RidersSunrisers Hyderabad101VR IyerShahbaz AhmedSS Iyer101NaN0NaNNaNNaN
26091814263122Kolkata Knight RidersSunrisers Hyderabad102SS IyerShahbaz AhmedVR Iyer101NaN0NaNNaNNaN
26091914263122Kolkata Knight RidersSunrisers Hyderabad103VR IyerShahbaz AhmedSS Iyer101NaN0NaNNaNNaN